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@karpathy
karpathy / llm-wiki.md
Created April 4, 2026 16:25
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@rohitg00
rohitg00 / llm-wiki.md
Last active April 13, 2026 01:13 — forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

@karpathy
karpathy / microgpt.py
Last active April 13, 2026 01:14
microgpt
"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
@baba-s
baba-s / Sprites-FillAmount.shader
Last active April 13, 2026 01:12
Shader "Sprites/Fill Amount"
{
Properties
{
[PerRendererData] _MainTex ("Sprite Texture", 2D) = "white" {}
[HideInInspector] _Color ("Tint", Color) = (1,1,1,1)
[MaterialToggle] PixelSnap ("Pixel snap", Float) = 0
[HideInInspector] _RendererColor ("RendererColor", Color) = (1,1,1,1)
[HideInInspector] _Flip ("Flip", Vector) = (1,1,1,1)
[PerRendererData] _AlphaTex ("External Alpha", 2D) = "white" {}
@logikal
logikal / darkmoon_plates.md
Last active April 13, 2026 01:12
Darkmoon Plate Settings Matrix
Plate Plate in OrcaSlicer Used For Cleaning Excels at Notes Mfr Link
Darkmoon Satin Textured PEI Plate, H2D: Smooth High Temp Plate PLA,PETG,TPU,ABS/ASA/PC/PA IPA/Dawn Workhorse plate for almost everything Link
Darkmoon CFX (Carbon Fiber) Smooth High Temp Plate PLA,PETG,TPU,ABS/ASA/PC/PA IPA/Dawn En
@rafaelquintanilha
rafaelquintanilha / slack-ai-teammates-openclaw.md
Created February 24, 2026 19:05
Running Multiple AI Agents as Slack Teammates via OpenClaw

Running Multiple AI Agents as Slack Teammates via OpenClaw

Goal: Run multiple AI agents as real Slack teammates — each with its own identity, able to DM independently or participate in shared channels — while keeping orchestration centralized through one primary agent.

Architecture

Use one Slack app/bot per agent identity. For example, if you have four agents:

Agent role OpenClaw agent ID Slack bot name